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As with a number of measures that have recently called our traditional models into question and the way we understand economic activity, the FRED Blog suggests there may be limitations to some of the mechanisms we have used for more than seventy years:

GDP has been used as a measure of economic well-being since the 1940s: It measures the total economic output by individuals, businesses, and the government and is a tangible way to quantify the state of the economy. However, some economists have questioned how well GDP measures well-being: For example, GDP fails to account for the quality of goods and services, the depletion of natural resources, and unpaid jobs that are nevertheless important (e.g., household chores). Although this criticism may be well founded, GDP is highly correlated with other measures of well-being, such as life expectancy at birth and the infant mortality rate, both of which capture some aspects of quality of life.

It’s a self-obviating point that developed nations would have much “higher levels of per capita GDP have, on average, higher levels of income and consumption,” or purchasing power. But other factors weigh into the question of how well off we are in terms of quality of life. Measures such as life expectancy and general health add to the discussion of well-being.

See the interactive map below for a “correlation between GDP and other measures of well-being” where GDP is “still a reasonable proxy of the overall well-being” for any given economy:

In an interesting post from the FRED Blog, Healthy inflation?Inflation in the healthcare industry vs. general CPI, a comparison is set up between elements of the consumer price index, versus the rate of rising costs related to healthcare. The authors point out (what most of us have known for decades) that medical care has risen faster than the other components in the CPI basket:

Going back as far as the series are available, since 1948, the price of medical care has grown at an average annual rate of 5.3% while the entire basket, headline CPI, has grown at an average annual rate of 3.5%. In the past 20 years, in the regime of stable inflation, headline CPI has grown at an average annual rate of 2.2%, whereas the price level of medical care has grown at an average annual rate of 3.6%—about 70% faster.

The post continues addressing why this matters, beyond the obvious and anecdotal, namely, policy implications, impact to the average consumer, retirees and those with stagnant wages:

The implication of these two features is far reaching: It’s symptomatic of the increasing share of income the U.S. spends on medical care. Beyond macro trends, the features of these two series themselves have policy implications. Indeed, indexing government healthcare budgets to overall CPI rather than medical care prices has implications for spending in real terms. This gap could also widen during recessions, when government help may be most in demand.

This does not bode well given current policy discussions, as noted in the Wall Street Journal, “any replacement health plan that satisfied GOP conservatives was likely to be opposed by the party’s centrists, and vice versa.” See the full FRED post here.

According to the Philadelphia Fed’s Real-Time Data Research Center, the outlook for 2017 is slightly upbeat, particularly compared to a few months back:

The U.S. economy over the next four quarters looks slightly stronger now than it did three months ago…forecasters predict real GDP will grow at an annual rate of 3.1 percent this quarter, up from the previous estimate of 2.3 percent. Quarterly growth over the following three quarters also looks improved. On an annual-average over annual-average basis, the forecasters predict real GDP will grow 2.1 percent in 2017, 2.5 percent in 2018, 2.1 percent in 2019, and 2.3 percent in 2020.

An improved outlook for the unemployment rate accompanies the outlook for growth. The forecasters predict that the unemployment rate will average 4.5 percent in the current quarter, before falling to 4.4 percent in the next two quarters, and 4.3 percent in the first two quarters of 2018. The projections for the next four quarters (and the next four years) are below those of the last survey, indicating a brighter outlook for unemployment.

The forecasters assign the following mean probability to GDP growth rates this year:

Note on Inflation

One persistent element is the inflation outlook in the coming years. The forecasters note a downward revision:

The forecasters expect current-quarter headline CPI inflation to average 1.6 percent, lower than the last survey’s estimate of 2.3 percent. Similarly, the forecasters predict current-quarter headline PCE inflation of 1.2 percent, also lower than the 2.0 percent predicted three months ago.

Measured on a fourth-quarter over fourth-quarter basis, headline CPI inflation is expected to average about 2.3 percent in each of the next three years, little changed from the last survey. The forecasters have revised downward their projections for headline PCE inflation in 2017 to 1.8 percent, but they pegged the rates for 2018 and 2019 at 2.0 percent, unchanged from the last survey.

Over the next 10 years, 2017 to 2026, the forecasters expect headline CPI inflation to average 2.30 percent at an annual rate, unchanged from the last survey. The corresponding estimate for 10-year annual-average headline PCE inflation is 2.09 percent, little changed from the 2.10 percent predicted in the previous survey.

While not completely unexpected, this inflation forecast demonstrates an interesting shift, especially given the state of full employment. See the full writeup with lots of stats here.

Per the Wall Street Journal, Federal Reserve Chairwoman Janet Yellen sees enough strength in the economy to continue the process of normalizing interest rates over this year and the next, after finally topping a 2.1% inflation target in February. The Fed chair reports growth, but “at a modest pace.” As such, the policy calls for action seeking a middle ground:

Where before we had our foot pressed down on the gas pedal trying to give the economy all the oomph we possibly could, now [we’re] allowing the economy to kind of coast and remain on an even keel,” she said. “To give it some gas, but not so much that we’re pressing down hard on the accelerator.”

Another interesting note is what may happen to the Fed’s $4.5 trillion portfolio:

Fed officials raised rates in March for only the third time since the financial crisis, to a range between 0.75% and 1%. But they have penciled in two more rate increases this year, followed by three in 2018. They are also considering reducing the Fed’s $4.5 trillion portfolio of cash and securities, acquired during three rounds of asset purchases aimed at lowering long-term borrowing costs after the recession.

It also seems the inflation target is going hold at 2%, which may be much more realistic in the long term, per the chair, “Evidence suggests that the population roughly expects inflation in the vicinity of 2%.”

Cargo volumes at the Port of Los Angeles reached 8,856,782 Twenty-Foot Equivalent Units in 2016, marking the busiest year ever for a Western Hemisphere Port. The previous record was set in 2006, when the Port of Los Angeles handled 8,469,853 TEUs.

Attributed to the success is cited as understanding:

…”the importance of innovating and collaborating to move our economy forward,” said Mayor Eric Garcetti. “We have seen incredible progress over the last two years, and it speaks to the hard work and partnership between the City, business leaders, and the workers who keep our port running smoothly every day.”

The Port finished the year strong, with December volumes of 796,536 TEUs, a 27 percent increase compared to the same period last year. It was the Port’s busiest December and fourth quarter in its 110-year history. Overall in 2016, cargo increased 8.5 percent compared to 2015.

The end of the calendar year 2016 showed the following shipment activity:

To put this milestone into perspective, look at the same time span filtered to total containers only (this illustrates the long crawl forward since the mid-2000s):

The Port of Long Beach had a little different year, but still posted strong results in spite of challenges:

Slowed by industry headwinds and challenges that included a major customer declaring bankruptcy, the Port of Long Beach still moved almost 6.8 million containers in 2016, its fifth best year ever.

Overall cargo declined 5.8 percent in 2016 compared to 2015, as the Port was impacted by new ocean carrier alliances and the August bankruptcy of Hanjin Shipping, a South Korean company and former majority stakeholder at the 381-acre Pier T container terminal — Long Beach’s largest.

By year’s end, the Harbor Commission had approved an agreement for a subsidiary of Mediterranean Shipping Co., one of the world’s largest container ship operators, to take sole control of the long-term lease at Pier T.

…“Last year was turbulent, with numerous ocean carrier mergers and other changes,” said Harbor Commission President Lori Ann Guzmán. “Now we have one of the largest ocean carriers in the world as a major partner and we’re well positioned to rebound in 2017. While the industry strives for equilibrium, Long Beach will continue be a reliable port of entry and continue to provide the fastest, most efficient services for trade from the Far East.”

Again, the change in volume since the mid-2000s is even more felt when viewing total containers only:

Irrational exuberance has been one of the most iconic and recognizable phrases in the financial markets for the last twenty years – to the day. I remember this like it was yesterday, being a recent graduate and shortly after, working in the capital market. This really was the advent of an era where there has been no looking back: a tenuous and ambivalent relationship with the Fed and every nuance uttered by the Chair. Here is the full quote in its context:

Clearly, sustained low inflation implies less uncertainty about the future, and lower risk premiums imply higher prices of stocks and other earning assets. We can see that in the inverse relationship exhibited by price/earnings ratios and the rate of inflation in the past. But how do we know when irrational exuberance has unduly escalated asset values, which then become subject to unexpected and prolonged contractions as they have in Japan over the past decade? And how do we factor that assessment into monetary policy? We as central bankers need not be concerned if a collapsing financial asset bubble does not threaten to impair the real economy, its production, jobs, and price stability. Indeed, the sharp stock market break of 1987 had few negative consequences for the economy. But we should not underestimate or become complacent about the complexity of the interactions of asset markets and the economy. Thus, evaluating shifts in balance sheets generally, and in asset prices particularly, must be an integral part of the development of monetary policy.

Interestingly enough, here is some commentary in our present time declaring, “rationally exuberant,” (caveat emptor on long positions if you ask me):

Not everyone is convinced of this view to be sure:

In recent years the Fed has only doubled down on these policies by directly pursuing a “wealth effect.” Rather than give a boost to the broad economy, however, these central bankers have only accomplished an even greater and more pervasive financial asset perversion. Stocks, bonds and real estate have all become as overvalued as we have ever seen any one of them individually in this country. The end result of all of this money printing and interest rate manipulation is the worst economic expansion since the Great Depression and the greatest wealth inequality since that period, as well.

The FRED Blog has an interesting assessment of unemployment, as measured by the 4-Week Moving Average of Initial Claims, Civilian Unemployment Rate and Average (Mean) Duration of Unemployment:

Take note especially of the Average (Mean) Duration of Unemployment – this corresponds to the “Scariest jobs chart ever” at Calculated Risk. From the FRED Blog using the analogy of the “unemployment bathtub”:

Economists often find a bathtub to be a useful metaphor for the behavior of unemployment. There’s some inflow of newly unemployed workers and some outflow as workers find jobs. A classic way to measure the inflow has been with initial claims of unemployment benefits, the blue line, in which we see spikes at the start of each recession. This inflow of newly unemployed persons initially reduces the mean duration of unemployment, the green line. But the green duration line rises as the blue initial claims line falls—since people who become unemployed early in the recession and remain so are unemployed for a while by the time the recession winds down. Every recession follows this pattern: Claims peak, then unemployment peaks, then duration peaks. The logic is essentially that of the bathtub: First it fills quickly; then, after some time, it begins to drain. But as this is happening, those left in tub have been there longer and longer.

The alarming measurement was just how long it took to reach pre-recession peak levels of jobs lost – a level reached “April 2014 with revisions.” Since we have met and exceeded this level for some time, the concerns now turn to issues such as the levels of employment (part-time temporary vs. full-time) as well as the “quality of jobs.”

Using the very simple computation, Compensation of Employees/Gross Domestic Product, FRED data shows some very interesting results over the last five decades:

Since the late 1960s, each run up in this measurement seems to be testing a high in the short run, then is followed by new declines. In some cases, sustained declines, with the last significant run up between the years of 1995-2000. The biggest question is why. The FRED Blog is the first to note that understanding this would require:

Analysis of (i) supplements to wages and salaries such as pensions and other benefits and (ii) proprietors’ income, which is earned by independent workers and business owners that compensates for labor and capital. What we are interested in is whether the decline has bottomed out.

Where are we now in this trend? Again, it is noted in the post, “that call is difficult. If you play with the graph by changing dates—for example, by ending the data in the year 2000 or 1987—you’d find a pretty similar situation in which the decline appears to have reversed.” What is also interesting is the proximity of the short-term high measurements to recessions.

The GDPNow model forecast for real GDP growth (seasonally adjusted annual rate) in the second quarter of 2016 is 2.5 percent on May 17, down from 2.8 percent on May 13. The second-quarter forecast for real residential investment growth declined from 5.3 to 2.5 percent after this morning’s housing starts release from the U.S. Census Bureau, the forecast for real consumer spending growth ticked down from 3.7 percent to 3.6 percent after this morning’s Consumer Price Index release from the U.S. Bureau of Labor Statistics, and the forecast for the contribution of inventory investment to second-quarter growth declined from -0.24 percentage points to -0.39 percentage points after this morning’s industrial production release from the Federal Reserve. The latter decline was concentrated in motor vehicle and parts dealers’ inventories.

The FRED Blog posted a very interesting dataset that illustrates the sway that public policy holds on public behavior, spending decisions and in this case, wealth preservation, “taxpayers adjusted their various income streams by trying to shift income from the beginning of 2013 to the end of 2012. This shift applies primarily to capital income.” The results are illustrated below in a customizable FRED graph:

Other comments in the post help explain the variance between two identical datasets (with the same label):

Both lines have the same title, real disposable personal income per capita, and yet they look very different. The extra careful reader will notice one series has a yearly frequency and the other has a monthly frequency. Here, frequency matters a lot, but not because of the usual concerns about seasonality. Income climbs steeply at the end of 2012 before falling dramatically in January 2013. This has to do with the so-called fiscal cliff: A series of temporary income tax cuts were set to expire on December 31, 2012, increasing the tax rate on personal income for many people in potentially significant ways.

This is quite interesting as it relates to this particular variance, but take a look at the same data from the last sixteen years:

What explains the variances showing very similar patterns? (Including a spike/cliff right in the middle of the Great Crash of 2008.) See the full post here.

Today’s Advance Monthly Sales for Retail and Food Services (12/2015) supports the anecdotal activity that many are observing: modest but forward movement. And good news is good news, perhaps especially this far into expansion. A look at retail sales growth excluding autos shows about 26% growth over the long haul of this economic expansion:

Chronologically, this expansion is old. It has now lasted into its 77th month, making it the fifth longest of the 34 expansions since 1900. Looking at light-vehicle sales, at over 18 million units annualized in each of the last two months, we should be close to a peak. Housing starts, however, remain well below their long-term averages, suggesting years of expansion to come, while interest rates and inflation are at levels normally associated with early expansion. Moreover, all of these measures are severely distorted by extremely aggressive monetary easing.

However, there is one “North Star” variable that has behaved in almost the same way in all modern expansions, namely, the unemployment rate….The November employment report confirmed that the unemployment rate has now been falling for six years at a steady pace of 0.8% per year and has now declined to 5.0%. Slow labor force growth and steady economic growth suggests a continuation of this pace, which would put the unemployment rate at 4.2% in November of 2016 and 3.8% in the spring of 2017.

With these measurements of employment, it is interesting that consumer behavior has not snapped back at the same pace as in previous recoveries. For example, compare the same data during the expansion cycle after the brief recession following the dot-com correction:

Now look at the blistering pace for the previous decade:

Notice the decline (or less steepness) with each recovery cycle? Put another way, the percentage increases for the above three cited time spans of expansion is (beginning): 1992: 61%, 2001: 41%, 2006: 26%.

The suggestion that wages have not kept time with costs, even as unemployment has continued to decline is unlikely to surprise anyone, such as the following commentary in the Economist:

Low unemployment means that employers have to try harder to find new workers, while existing workers can threaten to move elsewhere. As a result, workers should be able to demand higher wages. Yet firms in America seem not to have got the message. Inflation-adjusted wages for typical workers are stagnant. In fact, they have barely grown in the past five years; average hourly earnings rose 2% year-on-year in February of 2015: about the same as in February of 2010.

FRED demonstrates this same wage data as a trend, that since the 1980’s has diverged from GDP. The graph below shows the same data as comparative indexes where “it’s immediately apparent that the GDP figure is now higher than wages, meaning that it has grown faster since the 1980s:”

The post notes this caveat when trying to aggregate wages:

It’s not totally obvious how we should define wages—because wage dynamics change so much over the distribution. Low, medium, and high wages have grown at different rates and at different times. From a macroeconomic perspective, however, it makes some sense to measure the average wage. The effect of so doing is that we put more weight on the higher earners than the average person, a result of a positively skewed wage distribution. (Recall the definition of skewness: Here, it means the top tail can pull up the mean past the average person’s wage, the median wage.)

But why? According to the same post, both GDP and BLS data “plummeted in the Great Recession, but since then have been growing at about the same pace. The decline in wages as a fraction of GDP is not a result of a sluggish recovery from the Great Recession, but rather from effects predating it.” This still does not explain why, but more or less, what has happened. Again, the same post in the Economist suggests a “wage hangover,” where “firms preferred to return to more normal management conditions, and to let too-high wages adjust over time: “pent-up” wage cuts have been achieved simply by not granting raises. Wages, in other words, are not rising by more because in many cases they are already too high.” Wow! That is so simple yet reasonable, it may very well be a significant factor contributing to this trend. Another factor cited is what many of us know anecdotally as well as from the data: “part-time for economic reasons” and other forms of un- or under employment.